Part 5: Training the Model
We train the model using the
#Training #MachineLearning #ModelFit
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Part 6: Evaluating and Discussing Results
After training, we evaluate the model's performance on the test set. We also plot the training history to visualize accuracy and loss curves. This helps us understand if the model is overfitting or underfitting.
Discussion:
The plots show how accuracy and loss change over epochs. Ideally, both training and validation accuracy should increase, while losses decrease. If the validation accuracy plateaus or decreases while training accuracy continues to rise, it's a sign of overfitting. Our simple model achieves a decent accuracy. To improve it, one could use techniques like Data Augmentation, Dropout layers, or a deeper architecture.
#Evaluation #Results #Accuracy #Overfitting
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Part 7: Making Predictions on a Single Image
This is how you handle a single image file for prediction. The model expects a batch of images as input, so we must add an extra dimension to our single image before passing it to
#Prediction #ImageProcessing #Inference
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By: @DataScienceM ✨
We train the model using the
fit() method, providing our training data, batch size, number of epochs, and validation data to monitor performance on unseen data.history = model.fit(x_train, y_train,
epochs=15,
batch_size=64,
validation_data=(x_test, y_test))
#Training #MachineLearning #ModelFit
---
Part 6: Evaluating and Discussing Results
After training, we evaluate the model's performance on the test set. We also plot the training history to visualize accuracy and loss curves. This helps us understand if the model is overfitting or underfitting.
# Evaluate the model on the test data
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f'\nTest accuracy: {test_acc:.4f}')
# Plot training & validation accuracy values
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
# Plot training & validation loss values
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
Discussion:
The plots show how accuracy and loss change over epochs. Ideally, both training and validation accuracy should increase, while losses decrease. If the validation accuracy plateaus or decreases while training accuracy continues to rise, it's a sign of overfitting. Our simple model achieves a decent accuracy. To improve it, one could use techniques like Data Augmentation, Dropout layers, or a deeper architecture.
#Evaluation #Results #Accuracy #Overfitting
---
Part 7: Making Predictions on a Single Image
This is how you handle a single image file for prediction. The model expects a batch of images as input, so we must add an extra dimension to our single image before passing it to
model.predict().# Select a single image from the test set
img_index = 15
test_image = x_test[img_index]
true_label_index = np.argmax(y_test[img_index])
# Display the image
plt.imshow(test_image)
plt.title(f"Actual Label: {class_names[true_label_index]}")
plt.show()
# The model expects a batch of images, so we add a dimension
image_for_prediction = np.expand_dims(test_image, axis=0)
print("Image shape before prediction:", test_image.shape)
print("Image shape after adding batch dimension:", image_for_prediction.shape)
# Make a prediction
predictions = model.predict(image_for_prediction)
predicted_label_index = np.argmax(predictions[0])
# Print the result
print(f"\nPrediction Probabilities: {predictions[0]}")
print(f"Predicted Label: {class_names[predicted_label_index]}")
print(f"Actual Label: {class_names[true_label_index]}")
#Prediction #ImageProcessing #Inference
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By: @DataScienceM ✨
📌 Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-22 | ⏱️ Read time: 4 min read
Mastering the bias-variance trade-off is key to effective machine learning. Overfitting creates models that memorize training data noise and fail to generalize, while underfitting results in models too simple to find patterns. The optimal model exists in a "sweet spot," balancing complexity to perform well on new, unseen data. This involves learning just the right amount from the training set—not too much, and not too little—to achieve strong predictive power.
#MachineLearning #DataScience #Overfitting #BiasVariance
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-22 | ⏱️ Read time: 4 min read
Mastering the bias-variance trade-off is key to effective machine learning. Overfitting creates models that memorize training data noise and fail to generalize, while underfitting results in models too simple to find patterns. The optimal model exists in a "sweet spot," balancing complexity to perform well on new, unseen data. This involves learning just the right amount from the training set—not too much, and not too little—to achieve strong predictive power.
#MachineLearning #DataScience #Overfitting #BiasVariance
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⚡️ How does regularization prevent overfitting?
📈 #machinelearning algorithms have revolutionized the way we solve complex problems and make predictions. These algorithms, however, are prone to a common pitfall known as #overfitting. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. As a result, the model performs poorly on unseen data, leading to inaccurate predictions.
📈 To combat overfitting, #regularization techniques have been developed. Regularization is a method that adds a penalty term to the loss function during the training process. This penalty term discourages the model from fitting the training data too closely, promoting better generalization and preventing overfitting.
📈 There are different types of regularization techniques, but two of the most commonly used ones are L1 regularization (#Lasso) and L2 regularization (#Ridge). Both techniques aim to reduce the complexity of the model, but they achieve this in different ways.
📈 L1 regularization adds the sum of absolute values of the model's weights to the loss function. This additional term encourages the model to reduce the magnitude of less important features' weights to zero. In other words, L1 regularization performs feature selection by eliminating irrelevant features. By doing so, it helps prevent overfitting by reducing the complexity of the model and focusing only on the most important features.
📈 On the other hand, L2 regularization adds the sum of squared values of the model's weights to the loss function. Unlike L1 regularization, L2 regularization does not force any weights to become exactly zero. Instead, it shrinks all weights towards zero, making them smaller and less likely to overfit noisy or irrelevant features. L2 regularization helps prevent overfitting by reducing the impact of individual features while still considering their overall importance.
📈 Regularization techniques strike a balance between fitting the training data well and keeping the model's weights small. By adding a regularization term to the loss function, these techniques introduce a trade-off that prevents the model from being overly complex and overly sensitive to the training data. This trade-off helps the model generalize better and perform well on unseen data.
📈 Regularization techniques have become an essential tool in the machine learning toolbox. They provide a means to prevent overfitting and improve the generalization capabilities of models. By striking a balance between fitting the training data and reducing complexity, regularization techniques help create models that can make accurate predictions on unseen data.
📚 Reference: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
https://t.iss.one/DataScienceM⛈ ⚡️ ⚡️ ⚡️ ⚡️
📈 #machinelearning algorithms have revolutionized the way we solve complex problems and make predictions. These algorithms, however, are prone to a common pitfall known as #overfitting. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. As a result, the model performs poorly on unseen data, leading to inaccurate predictions.
📈 To combat overfitting, #regularization techniques have been developed. Regularization is a method that adds a penalty term to the loss function during the training process. This penalty term discourages the model from fitting the training data too closely, promoting better generalization and preventing overfitting.
📈 There are different types of regularization techniques, but two of the most commonly used ones are L1 regularization (#Lasso) and L2 regularization (#Ridge). Both techniques aim to reduce the complexity of the model, but they achieve this in different ways.
📈 L1 regularization adds the sum of absolute values of the model's weights to the loss function. This additional term encourages the model to reduce the magnitude of less important features' weights to zero. In other words, L1 regularization performs feature selection by eliminating irrelevant features. By doing so, it helps prevent overfitting by reducing the complexity of the model and focusing only on the most important features.
📈 On the other hand, L2 regularization adds the sum of squared values of the model's weights to the loss function. Unlike L1 regularization, L2 regularization does not force any weights to become exactly zero. Instead, it shrinks all weights towards zero, making them smaller and less likely to overfit noisy or irrelevant features. L2 regularization helps prevent overfitting by reducing the impact of individual features while still considering their overall importance.
📈 Regularization techniques strike a balance between fitting the training data well and keeping the model's weights small. By adding a regularization term to the loss function, these techniques introduce a trade-off that prevents the model from being overly complex and overly sensitive to the training data. This trade-off helps the model generalize better and perform well on unseen data.
📈 Regularization techniques have become an essential tool in the machine learning toolbox. They provide a means to prevent overfitting and improve the generalization capabilities of models. By striking a balance between fitting the training data and reducing complexity, regularization techniques help create models that can make accurate predictions on unseen data.
📚 Reference: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
https://t.iss.one/DataScienceM
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